RadBERT

RadBERT is a series of models trained with millions (more to come!) radiology reports, which achieves stronger medical language understanding performance than previous bio-medical domain models such BioBERT, Clinical-BERT, BLUE-BERT and BioMed-RoBERTa.

For details, check out the paper here: RadBERT: Adapting transformer-based language models to radiology

Pretrained Models

RadBERT-RoBERTa-4m is trained with RoBERTa initialization and 4 million VA hospital reports. You can access the model on huggingface from here.

How to use

Here is an example of how to use this model to extract the features of a given medical report in PyTorch:

from transformers import AutoConfig, AutoTokenizer, AutoModel
config = AutoConfig.from_pretrained('zzxslp/RadBERT-RoBERTa-4m')
tokenizer = AutoTokenizer.from_pretrained('zzxslp/RadBERT-RoBERTa-4m')
model = AutoModel.from_pretrained('zzxslp/RadBERT-RoBERTa-4m', config=config)
text = "Replace me by any medical text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

BibTeX entry and citation info

If you find this repository helpful, please cite our paper:

@article{yan2022radbert,
  title={RadBERT: Adapting transformer-based language models to radiology},
  author={Yan, An and McAuley, Julian and Lu, Xing and Du, Jiang and Chang, Eric Y and Gentili, Amilcare and Hsu, Chun-Nan},
  journal={Radiology: Artificial Intelligence},
  volume={4},
  number={4},
  pages={e210258},
  year={2022},
  publisher={Radiological Society of North America}
}